mlsanityData to 14 Oct 2026

AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale

arXiv 2505.08311 ↗First posted 13 May 2025cs.CLcited by 29

On the map: LLM reasoning → Reasoning benchmarks and evaluation

also in 1 more
Abstract (arXiv)

We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-R1 and rivaling leading Mixture-of-Experts (MoE) models like Qwen3-235B-A22B and Seed1.5-Thinking, AM-Thinking-v1 achieves impressive scores of 85.3 on AIME 2024, 74.4 on AIME 2025, and 70.3 on LiveCodeBench, showcasing state-of-the-art mathematical and coding capabilities among open-source models of similar scale. Built entirely from the open-source Qwen2.5-32B base model and publicly available queries, AM-Thinking-v1 leverages a meticulously crafted post-training pipeline - combining supervised fine-tuning and reinforcement learning - to deliver exceptional reasoning capabilities. This work demonstrates that the open-source community can achieve high performance at the 32B scale, a practical sweet spot for deployment and fine-tuning. By striking a balance between top-tier performance and real-world usability, we hope AM-Thinking-v1 inspires further collaborative efforts to harness mid-scale models, pushing reasoning boundaries while keeping accessibility at the core of innovation. We have open-sourced our model on hrefhttps://huggingface.co/a-m-team/AM-Thinking-v1Hugging Face.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 7 of its 59 references that are in the map. First posted on arXiv 13 May 2025.

Builds on

7 of 59 references.

Closest ancestors · 1

Same direction, newest first

  1. 20251.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training25 Mar

Other references · 6

  1. 2024Qwen2 Technical Reportcited by 2,120
  2. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  3. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 2,004
  4. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  5. 2023MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Modelscited by 735
  6. 2024Free Process Rewards without Process Labelscited by 165

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 17; 2026: 12.

29 papers.

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  1. 2025Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routerscited by 72
  2. 2025No Free Lunch: Rethinking Internal Feedback for LLM Reasoningcited by 37
  3. 2026MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methodscited by 30
  4. 2025Not All Correct Answers Are Equal: Why Your Distillation Source Matterscited by 25
  5. 2025Towards Concise and Adaptive Thinking in Large Reasoning Models: A Surveycited by 18
  6. 2025ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shapingcited by 17
  7. 2025AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agentcited by 17
  8. 2026Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoningcited by 17
  9. 2025MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimizationcited by 12
  10. 2025K2-Think: A Parameter-Efficient Reasoning Systemcited by 11
  11. 2025Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thoughtcited by 10
  12. 2025Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?cited by 5
17 more
  1. 2025Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Modelscited by 5
  2. 2026Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verificationcited by 5
  3. 2025OPV: Outcome-based Process Verifier for Efficient Long Chain-of-Thought Verificationcited by 4
  4. 2025Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasetscited by 4
  5. 2025Best-of-∞ -- Asymptotic Performance of Test-Time LLM Ensemblingcited by 3
  6. 2026How Does the Thinking Step Influence Model Safety? An Entropy-based Safety Reminder for LRMscited by 3
  7. 2025JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Modelscited by 2
  8. 2025Sticker-TTS: Learn to Utilize Historical Experience with a Sticker-driven Test-Time Scaling Frameworkcited by 2
  9. 2025Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVRcited by 2
  10. 2026Recursive Think-Answer Process for LLMs and VLMscited by 2
  11. 2026Efficient Multilingual Reasoning Transfer via Progressive Code-Switchingcited by 2
  12. 2026PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learningcited by 1
  13. 2026A Primer in Post-Training Reasoning Data: What We Know About How It Workscited by 1
  14. 2026CausalMix: Data Mixture as Causal Inference for Language Model Trainingcited by 0
  15. 2026MMTClinic: Multimodal, Multilingual Time Series Question Answering and Reasoning Benchmark for Clinical Domaincited by 0
  16. 2026Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoningcited by 0
  17. 2026Rufus-Air: An Open LLM Post-Training Recipecited by 0

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  1. 2025Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
  2. 2024OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models
  3. 2026Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
  4. 2025From System 1 to System 2: A Survey of Reasoning Large Language Models
  5. 2024A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
  6. 2025MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
  7. 2025Large Language Models Imitate Logical Reasoning, but at what Cost?
  8. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models